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Genetic (co)variance components for slaughter traits in a multi-breed sheep population
N McHugh1, T Pabiou2, K McDermott2
1Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland.
This study found significant genetic variability in sheep carcass traits and slaughter age, enabling genetic merit prediction using live weight measurements. These findings are crucial for improving profitability in meat sheep enterprises.
Area of Science:
- Animal Genetics
- Quantitative Genetics
- Livestock Production
Background:
- Carcass value and age at slaughter significantly impact meat sheep enterprise revenue and production costs.
- Limited understanding exists regarding the exploitable genetic variability in traits influencing carcass value (weight, conformation, fat) and slaughter age.
- Estimating genetic (co)variances is essential for optimizing breeding strategies.
Purpose of the Study:
- To estimate genetic (co)variances for carcass weight, conformation, fat, kill-out percentage, and age at slaughter.
- To assess genetic (co)variances between these traits and earlier-life measurements.
- To determine the extent of exploitable genetic variability in economically important sheep traits.
Main Methods:
- Analysis of slaughter records from 15,714 lambs, including live weight measurements for 12,630.
- Estimation of heritability and genetic correlations for carcass and live weight traits.
- Calculation of genetic coefficients of variation for key traits.
Main Results:
- Heritability estimates ranged from 0.08 (carcass fat) to 0.22 (kill-out percentage).
- Significant genetic correlations were found between live weight and carcass weight post-weaning (0.83).
- Live weight measurements accurately predict genetic merit for carcass traits and slaughter age.
Conclusions:
- Substantial exploitable genetic variability exists for sheep slaughter traits.
- Live weight measurements taken earlier in life can be used for genetic selection of carcass traits and slaughter age.
- Accurate genetic merit prediction is achievable, enhancing selection efficiency in sheep breeding programs.
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